import cv2 import numpy as np import torch def compute_increased_bbox(bbox, increase_area, preserve_aspect=True): left, top, right, bot = bbox width = right - left height = bot - top if preserve_aspect: width_increase = max(increase_area, ((1 + 2 * increase_area) * height - width) / (2 * width)) height_increase = max(increase_area, ((1 + 2 * increase_area) * width - height) / (2 * height)) else: width_increase = height_increase = increase_area left = int(left - width_increase * width) top = int(top - height_increase * height) right = int(right + width_increase * width) bot = int(bot + height_increase * height) return (left, top, right, bot) def get_valid_bboxes(bboxes, h, w): left = max(bboxes[0], 0) top = max(bboxes[1], 0) right = min(bboxes[2], w) bottom = min(bboxes[3], h) return (left, top, right, bottom) def align_crop_face_landmarks(img, landmarks, output_size, transform_size=None, enable_padding=True, return_inverse_affine=False, shrink_ratio=(1, 1)): """Align and crop face with landmarks. The output_size and transform_size are based on width. The height is adjusted based on shrink_ratio_h/shring_ration_w. Modified from: https://github.com/NVlabs/ffhq-dataset/blob/master/download_ffhq.py Args: img (Numpy array): Input image. landmarks (Numpy array): 5 or 68 or 98 landmarks. output_size (int): Output face size. transform_size (ing): Transform size. Usually the four time of output_size. enable_padding (float): Default: True. shrink_ratio (float | tuple[float] | list[float]): Shring the whole face for height and width (crop larger area). Default: (1, 1). Returns: (Numpy array): Cropped face. """ lm_type = 'retinaface_5' # Options: dlib_5, retinaface_5 if isinstance(shrink_ratio, (float, int)): shrink_ratio = (shrink_ratio, shrink_ratio) if transform_size is None: transform_size = output_size * 4 # Parse landmarks lm = np.array(landmarks) if lm.shape[0] == 5 and lm_type == 'retinaface_5': eye_left = lm[0] eye_right = lm[1] mouth_avg = (lm[3] + lm[4]) * 0.5 elif lm.shape[0] == 5 and lm_type == 'dlib_5': lm_eye_left = lm[2:4] lm_eye_right = lm[0:2] eye_left = np.mean(lm_eye_left, axis=0) eye_right = np.mean(lm_eye_right, axis=0) mouth_avg = lm[4] elif lm.shape[0] == 68: lm_eye_left = lm[36:42] lm_eye_right = lm[42:48] eye_left = np.mean(lm_eye_left, axis=0) eye_right = np.mean(lm_eye_right, axis=0) mouth_avg = (lm[48] + lm[54]) * 0.5 elif lm.shape[0] == 98: lm_eye_left = lm[60:68] lm_eye_right = lm[68:76] eye_left = np.mean(lm_eye_left, axis=0) eye_right = np.mean(lm_eye_right, axis=0) mouth_avg = (lm[76] + lm[82]) * 0.5 eye_avg = (eye_left + eye_right) * 0.5 eye_to_eye = eye_right - eye_left eye_to_mouth = mouth_avg - eye_avg # Get the oriented crop rectangle # x: half width of the oriented crop rectangle x = eye_to_eye - np.flipud(eye_to_mouth) * [-1, 1] # - np.flipud(eye_to_mouth) * [-1, 1]: rotate 90 clockwise # norm with the hypotenuse: get the direction x /= np.hypot(*x) # get the hypotenuse of a right triangle rect_scale = 1 # TODO: you can edit it to get larger rect x *= max(np.hypot(*eye_to_eye) * 2.0 * rect_scale, np.hypot(*eye_to_mouth) * 1.8 * rect_scale) # y: half height of the oriented crop rectangle y = np.flipud(x) * [-1, 1] x *= shrink_ratio[1] # width y *= shrink_ratio[0] # height # c: center c = eye_avg + eye_to_mouth * 0.1 # quad: (left_top, left_bottom, right_bottom, right_top) quad = np.stack([c - x - y, c - x + y, c + x + y, c + x - y]) # qsize: side length of the square qsize = np.hypot(*x) * 2 quad_ori = np.copy(quad) # Shrink, for large face # TODO: do we really need shrink shrink = int(np.floor(qsize / output_size * 0.5)) if shrink > 1: h, w = img.shape[0:2] rsize = (int(np.rint(float(w) / shrink)), int(np.rint(float(h) / shrink))) img = cv2.resize(img, rsize, interpolation=cv2.INTER_AREA) quad /= shrink qsize /= shrink # Crop h, w = img.shape[0:2] border = max(int(np.rint(qsize * 0.1)), 3) crop = (int(np.floor(min(quad[:, 0]))), int(np.floor(min(quad[:, 1]))), int(np.ceil(max(quad[:, 0]))), int(np.ceil(max(quad[:, 1])))) crop = (max(crop[0] - border, 0), max(crop[1] - border, 0), min(crop[2] + border, w), min(crop[3] + border, h)) if crop[2] - crop[0] < w or crop[3] - crop[1] < h: img = img[crop[1]:crop[3], crop[0]:crop[2], :] quad -= crop[0:2] # Pad # pad: (width_left, height_top, width_right, height_bottom) h, w = img.shape[0:2] pad = (int(np.floor(min(quad[:, 0]))), int(np.floor(min(quad[:, 1]))), int(np.ceil(max(quad[:, 0]))), int(np.ceil(max(quad[:, 1])))) pad = (max(-pad[0] + border, 0), max(-pad[1] + border, 0), max(pad[2] - w + border, 0), max(pad[3] - h + border, 0)) if enable_padding and max(pad) > border - 4: pad = np.maximum(pad, int(np.rint(qsize * 0.3))) img = np.pad(img, ((pad[1], pad[3]), (pad[0], pad[2]), (0, 0)), 'reflect') h, w = img.shape[0:2] y, x, _ = np.ogrid[:h, :w, :1] mask = np.maximum(1.0 - np.minimum(np.float32(x) / pad[0], np.float32(w - 1 - x) / pad[2]), 1.0 - np.minimum(np.float32(y) / pad[1], np.float32(h - 1 - y) / pad[3])) blur = int(qsize * 0.02) if blur % 2 == 0: blur += 1 blur_img = cv2.boxFilter(img, 0, ksize=(blur, blur)) img = img.astype('float32') img += (blur_img - img) * np.clip(mask * 3.0 + 1.0, 0.0, 1.0) img += (np.median(img, axis=(0, 1)) - img) * np.clip(mask, 0.0, 1.0) img = np.clip(img, 0, 255) # float32, [0, 255] quad += pad[:2] # Transform use cv2 h_ratio = shrink_ratio[0] / shrink_ratio[1] dst_h, dst_w = int(transform_size * h_ratio), transform_size template = np.array([[0, 0], [0, dst_h], [dst_w, dst_h], [dst_w, 0]]) # use cv2.LMEDS method for the equivalence to skimage transform # ref: https://blog.csdn.net/yichxi/article/details/115827338 affine_matrix = cv2.estimateAffinePartial2D(quad, template, method=cv2.LMEDS)[0] cropped_face = cv2.warpAffine( img, affine_matrix, (dst_w, dst_h), borderMode=cv2.BORDER_CONSTANT, borderValue=(135, 133, 132)) # gray if output_size < transform_size: cropped_face = cv2.resize( cropped_face, (output_size, int(output_size * h_ratio)), interpolation=cv2.INTER_LINEAR) if return_inverse_affine: dst_h, dst_w = int(output_size * h_ratio), output_size template = np.array([[0, 0], [0, dst_h], [dst_w, dst_h], [dst_w, 0]]) # use cv2.LMEDS method for the equivalence to skimage transform # ref: https://blog.csdn.net/yichxi/article/details/115827338 affine_matrix = cv2.estimateAffinePartial2D( quad_ori, np.array([[0, 0], [0, output_size], [dst_w, dst_h], [dst_w, 0]]), method=cv2.LMEDS)[0] inverse_affine = cv2.invertAffineTransform(affine_matrix) else: inverse_affine = None return cropped_face, inverse_affine def paste_face_back(img, face, inverse_affine): h, w = img.shape[0:2] face_h, face_w = face.shape[0:2] inv_restored = cv2.warpAffine(face, inverse_affine, (w, h)) mask = np.ones((face_h, face_w, 3), dtype=np.float32) inv_mask = cv2.warpAffine(mask, inverse_affine, (w, h)) # remove the black borders inv_mask_erosion = cv2.erode(inv_mask, np.ones((2, 2), np.uint8)) inv_restored_remove_border = inv_mask_erosion * inv_restored total_face_area = np.sum(inv_mask_erosion) // 3 # compute the fusion edge based on the area of face w_edge = int(total_face_area**0.5) // 20 erosion_radius = w_edge * 2 inv_mask_center = cv2.erode(inv_mask_erosion, np.ones((erosion_radius, erosion_radius), np.uint8)) blur_size = w_edge * 2 inv_soft_mask = cv2.GaussianBlur(inv_mask_center, (blur_size + 1, blur_size + 1), 0) img = inv_soft_mask * inv_restored_remove_border + (1 - inv_soft_mask) * img # float32, [0, 255] return img if __name__ == '__main__': import os from fooocus_extras.facexlib.detection import init_detection_model from fooocus_extras.facexlib.utils.face_restoration_helper import get_largest_face from fooocus_extras.facexlib.visualization import visualize_detection img_path = '/home/wxt/datasets/ffhq/ffhq_wild/00009.png' img_name = os.splitext(os.path.basename(img_path))[0] # initialize model det_net = init_detection_model('retinaface_resnet50', half=False) img_ori = cv2.imread(img_path) h, w = img_ori.shape[0:2] # if larger than 800, scale it scale = max(h / 800, w / 800) if scale > 1: img = cv2.resize(img_ori, (int(w / scale), int(h / scale)), interpolation=cv2.INTER_LINEAR) with torch.no_grad(): bboxes = det_net.detect_faces(img, 0.97) if scale > 1: bboxes *= scale # the score is incorrect bboxes = get_largest_face(bboxes, h, w)[0] visualize_detection(img_ori, [bboxes], f'tmp/{img_name}_det.png') landmarks = np.array([[bboxes[i], bboxes[i + 1]] for i in range(5, 15, 2)]) cropped_face, inverse_affine = align_crop_face_landmarks( img_ori, landmarks, output_size=512, transform_size=None, enable_padding=True, return_inverse_affine=True, shrink_ratio=(1, 1)) cv2.imwrite(f'tmp/{img_name}_cropeed_face.png', cropped_face) img = paste_face_back(img_ori, cropped_face, inverse_affine) cv2.imwrite(f'tmp/{img_name}_back.png', img)